Cancer of Unknown Primary (CUP) represents a heterogeneous group of metastatic malignancies where the primary tumor site remains unidentified despite extensive clinical evaluation. This condition accounts for about 2–5% of all cancer diagnoses and is linked to a poor prognosis primarily due to diagnostic uncertainties and the reliance on empiric treatment approaches. Traditional methods, such as imaging, histopathology, and immunohistochemistry, often fall short, especially in cases of poorly differentiated tumors or those with atypical phenotypes. The loss of morphological features during metastasis further complicates accurate tissue identification, leading to challenges in tailoring effective therapies.
Molecular profiling has emerged as a vital adjunctive tool in addressing these challenges. Techniques like transcriptomic classifiers analyze gene expression patterns and compare them to validated reference datasets from known tumor types. Since metastatic tumors frequently retain lineage-specific transcriptional signatures, these molecular methods can propose a likely tissue of origin, enhancing diagnostic precision. This accuracy is crucial as it directly influences treatment selection, shifting from broad empiric therapies to more targeted, site-specific regimens, including chemotherapy and precision medicines.